MétaCan
Menu
Back to cohort
Record W4399766041 · doi:10.32920/26052835.v1

Characterization of Liposomal Release Temperature Using a Low Field Magnetic Resonance System

2024· preprint· en· W4399766041 on OpenAlexaff
Khalid Noori

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicFiber-reinforced polymer composites
Canadian institutionsToronto Metropolitan UniversityWestern University
Fundersnot available
KeywordsCharacterization (materials science)LiposomeMaterials scienceField (mathematics)Nuclear magnetic resonanceMagnetic fieldNanotechnologyPhysics

Abstract

fetched live from OpenAlex

The use of liposomes as chemotherapeutic carriers has been the subject of many research studies, with emphasis on enhancing the therapeutic index by increasing effect on cancer cells and reducing side effects. An important parameter to characterize for thermosensitive liposomes, TSLs, is the release temperature, Tr, the temperature at which liposomes release most of their content. By loading liposomes with a contrast agent such as Mn2+, a benchtop NMR system can be used to determine Tr through incremental heating. In this work, the release temperature was extracted from relaxometry measurements of Mn2+-loaded TSLs. Tr values were then compared with transition temperature values, Tm, the temperature at which liposomes’ bilayer changes state, obtained by a standard technique. The difference of the average values between Tr and Tm was approximately 0.5 oC. The discrepancy is due to artifacts produced by the standard technique. Nonetheless, Tm can be used to predict Tr.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.004
GPT teacher head0.187
Teacher spread0.182 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same topicFiber-reinforced polymer compositesFrench-language works237,207